Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED CORRESPONDENCE
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Status of Claims
Claims 1, 3, 4, 8, 10, 11, 15, 17, 18 have been amended.
Claims 2, 7, 9, 14, 16 have been cancelled.
No claims have been added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 – 6, 8, 10 – 13, 15, 16 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
obtaining (i) a first set of aerial images of a first set of properties, and (ii) an indication of a square footage of each of the first set of properties
extracting feature values for features of the first set of properties, wherein at least one of the feature values is for a feature which is extracted from the first set of aerial images;
deriving predictor variables from the feature values which are most likely to be predictive of square footage by performing feature reduction to avoid overfitting;
determine a square footage of a property using the features values of the first set of properties and the predictor variables of each of the first set of properties;
identifying a subject property different from the first set of properties;
receiving one or more aerial images of the subject property;
extracting predictor variables of the subject property, wherein at least one of the predictor variables is for the feature which is extracted from the one or more aerial images of the subject property; and
providing the square footage of the subject property for display.
The invention is directed towards the abstract idea of real estate evaluation and assessment, which corresponds to both “Mental Processes” and “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by humans, a human in their mind, and/or through the aid of pen and paper, e.g., having a human collect and look at a plurality of aerial images for a plurality of real estate properties, collect information that include an indication of the square footage of those plurality of properties, look/observe the images to extract some information about the properties, identify and collect aerial images of a property of interest, look/observe the image of the property of interest, compare the information between the plurality of properties and property of interest to determine, guess, estimate, or the like the square footage of the property of interest, creating a record/profile for the property, and displaying the results of the analysis along with its confidence level.
The limitations of:
obtaining (i) a first set of aerial images of a first set of properties, and (ii) an indication of a square footage of each of the first set of properties
extracting feature values for features of the first set of properties, wherein at least one of the feature values is for a feature which is extracted from the first set of aerial images;
deriving predictor variables from the feature values which are most likely to be predictive of square footage by performing feature reduction to avoid overfitting;
determine a square footage of a property using the features values of the first set of properties and the predictor variables of each of the first set of properties;
identifying a subject property different from the first set of properties;
receiving one or more aerial images of the subject property;
extracting predictor variables of the subject property, wherein at least one of the predictor variables is for the feature which is extracted from the one or more aerial images of the subject property; and
providing the square footage of the subject property for display,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and generic machine learning algorithm. That is, other than reciting a generic processor executing computer code stored on a computer medium and generic machine learning algorithm nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and generic machine learning algorithm in the context of this claim encompasses human collecting and looking at aerial images of real estate properties and indications of their square footage and compare them against aerial images of a property of interest and, based on the comparison, determine, guess, estimate, or the like the square footage of the property of interest, as well as creating a record/profile of the property and displaying the results of the analysis along with its confidence level. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and generic machine learning algorithm, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium and generic machine learning algorithm to communicate information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. comparing collected information of a plurality of real estate properties against a property of interest to determine, guess, estimate the square footage of a property of interest. The generic processor executing computer code stored on a computer medium and generic machine learning algorithm in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium can perform the insignificant extra solution steps of communicating information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium are merely being applied to perform the steps that can be performed in the human mind or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium and generic machine learning algorithm.
Although the claim recites “train a machine learning algorithm,” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a machine learning algorithm model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning algorithm is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards real estate property evaluation and the data associated with real estate properties to determine, guess, or estimate the square footage of a property of interest. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and re-training are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor executing computer code stored on a computer medium and generic machine learning algorithm to perform the steps of:
obtaining (i) a first set of aerial images of a first set of properties, and (ii) an indication of a square footage of each of the first set of properties
extracting feature values for features of the first set of properties, wherein at least one of the feature values is for a feature which is extracted from the first set of aerial images;
deriving predictor variables from the feature values which are most likely to be predictive of square footage by performing feature reduction to avoid overfitting;
determine a square footage of a property using the features values of the first set of properties and the predictor variables of each of the first set of properties;
identifying a subject property different from the first set of properties;
receiving one or more aerial images of the subject property;
extracting predictor variables of the subject property, wherein at least one of the predictor variables is for the feature which is extracted from the one or more aerial images of the subject property; and
providing the square footage of the subject property for display,
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 3 is directed towards the recitation of generic technology and applying it to the abstract idea to, somehow, determine the year when the property of interest was built.
Claim 4 is directed towards the recitation of generic technology and applying it to the abstract idea to, somehow, determine the garage size of the property of interest.
Claims 5, 6 are directed the insignificant extra solution activity of gathering and receiving information and describing the information.
The remaining claims are similar in scope to what has been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for determining, guessing, or estimating the square footage of a real estate property. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3 – 6, 8, 10 – 13, 15, 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rose et al. (US PGPub 2019/0333175 A1) in view of Lookabaugh (WO 2019/113397 A1).
In regards to claims 1, 8, 15, Rose discloses (Claim 1) a computer-implemented method for use in determining a property parameter of a subject property, the method comprising; (Claim 8) a computer system configured for use in determining a property parameter of a subject property, the computer system comprising one or more processors configured to; (Claim 15) a non-transitory computer-readable memory storing instructions thereon, that when executed by one or more processors, cause the one or more processors to:
obtaining, by one or more processors, (i) a first set of aerial images of a first set of properties, and (ii) an indication of a square footage of each of the first set of properties (¶ 12, 16, 75, 77, 98, 121, 122, 215, 227 wherein aerial images and an indication of the square footage of properties are received);
extracting, by the one or more processors, feature values for features of the first set of properties, wherein at least one of the feature values is for a feature which is extracted from the first set of aerial images (¶ 12, 16, 75, 77, 98, 121, 122, 215, 227 wherein a plurality of different features can be extracted from the aerial images, such as, but not limited to, square footage, renovations, additions that add square footage, and etc., which can further be obtained from other sources of information, such as, but not limited to, city records, MLS listings, social listings, and etc. The “values” for the features can be as simple as yes/no, exist/does not exist, has/does not have, numerical, a description, or any combination thereof.);
In regards to:
deriving, by the one or more processors, predictor variables from the feature values which are most likely to be predictive of square footage by performing feature reduction to avoid overfitting;
training, by the one or more processors, a machine learning algorithm to determine a square footage of a property using the predictor variables of the first set of properties and the square footage […]
(¶ 12, 13, 14, 15, 17, 18, 103, 104, 106, 121, 162, 180, 181, 182, 184, 215 wherein machine learning is trained to determine a property parameter, such as, but not limited to, the square footage of a property using data mining of external sources, such as, but not limited to, property records, listings, photographic images (e.g., satellite/aerial, street view, videos), characteristics/features/predictor variables of the home (determining that a garage has been converted to a “Granny Flat” by determining, i.e. predicting, that the home should have a garage and comparing this information to, for example, images to determine that the garage is no longer there, thereby increasing the livable square footage of the property), and etc. Rose further discloses that subsets of the feature values/predictor variables can be correlated with square footage by associating these two pieces of information with one another. The system also receives verified data and feeds back the verified data to improve its prediction over time.
With regards to, “performing feature reduction to avoid overfitting” the machine learning is trained based on commonalities amongst the plurality of real estate properties that would best predict a desired outcome for a real estate property of interest, thereby performing feature reduction because it is not using data that will not help with the desired prediction and avoid overfitting because, again, it is not using data that will not help with the desired prediction.);
identifying, by one or more processors, a subject property different from the first set of properties (¶ 5, 16, 77, 88 wherein a property of interest is identified, for example, via its street address; ¶ 16, 116, 179 wherein a property is identified based on, at least, street address and is different from the set of properties that this property will be compared against, i.e. the system can be trained using semi-supervised labels that can be determined using a clustering technique to find properties similar to those flagged by previous human annotated labels and previous semi-supervised labels.
NOTE: For the purposes of compact prosecution, the Examiner has provided an alternate interpretation in view of Lookabaugh in the event that the applicant does not agree with this analysis to teach that a machine learning model can be trained using data from other properties and applied to a subject property to compare specific common characteristics and allow the machine learning model to determine, estimate, calculate, or the like information not found in the subject property.);
receiving, at the one or more processors, one or more aerial images of the subject property (¶ 16 wherein aerial images of the property are received);
extracting, by the one or more processors, predictor variables of the subject property, wherein at least one of the predictor variables is for the feature which is extracted from the one or more aerial images of the subject property (¶ 12, 16, 75, 77, 98, 121, 122, 215, 227 wherein a plurality of different features can be extracted from the aerial images, such as, but not limited to, square footage, renovations, additions that add square footage, and etc., which can further be obtained from other sources of information, such as, but not limited to, city records, MLS listings, social listings, and etc.
¶ 12, 13, 14, 15, 17, 18, 103, 104, 106, 121, 162, 180, 181, 182, 184, 215 wherein machine learning is trained to determine a property parameter, such as, but not limited to, the square footage of a property using data mining of external sources, such as, but not limited to, property records, listings, photographic images (e.g., satellite/aerial, street view, videos), characteristics/features/predictor variables of the home (determining that a garage has been converted to a “Granny Flat” by determining, i.e. predicting, that the home should have a garage and comparing this information to, for example, images to determine that the garage is no longer there, thereby increasing the livable square footage of the property), and etc. Rose further discloses that subsets of the feature values/predictor variables can be correlated with square footage by associating these two pieces of information with one another. The system also receives verified data and feeds back the verified data to improve its prediction over time.
NOTE: For the purposes of compact prosecution, the Examiner has provided an alternate interpretation in view of Lookabaugh in the event that the applicant does not agree with this analysis to teach that a machine learning model can be trained on extracted data from other properties and applied to a subject property to compare specific common characteristics and allow the machine learning model to determine, estimate, calculate, or the like information not found in the subject property.);
applying, by the one or more processors, the predictor variables from the feature values of the subject property to the trained machine learning algorithm to determine a square footage of the subject property (¶ 12, 13, 14, 103, 104, 106, 119, 121, 154 wherein machine learning is trained to determine the square footage and qualitative build grade of a property using data mining of external sources, such as, but not limited to, property records, listings, photographic images (e.g., satellite/aerial, street view, videos), and etc., and applying the predicted variables that indicate what should or is predicted to be present in a property in order to determine a square footage of the property
NOTE: For the purposes of compact prosecution, the Examiner has provided an alternate interpretation in view of Lookabaugh in the event that the applicant does not agree with this analysis to teach that a machine learning model can be trained on extracted data from other properties and applied to a subject property to compare specific common characteristics and allow the machine learning model to determine, estimate, calculate, or the like information not found in the subject property.); and
providing, by the one or more processors, the square footage of the subject property for display
(Fig. 18, 23; ¶ 7, 88, 89, 119, 121, 215, 218, 220, 227, 230, 233 wherein the system generates and displays a profile of a subject property with the determined square footage that is based on the system’s confidence that a change has occurred, wherein the confidence is based on and increases in response to the system utilizing more and more external sources of information and/or simply having an inspector provide their findings by physically inspecting the property (which results in the system changing its initial confidence level that there is an X% probability of a change made to the property to a (for example)100% confidence that there is an X% probability of a change made to a property because building equipment, waste, and etc. has been witnessed). As another non-limiting example, the system determines a confidence level higher than 0% due to the system receiving an indication that a social media message with the description “Brand New Carport!” was tagged at a particular property, refers to the property’s profile that no previous history of such a renovation was made, and updates the property’s profile which prompts city officials to visit the property to determine that renovations have been made and issue a warning or fine for the unpermitted renovation, wherein the increase in confidence level also prompts the system to also determine the probability of a renovation having taken place. The system also compares its findings against a threshold and increases its confidence by searching through more external sources to determine if the probability of a renovation also increases. That is to say, the system searches and retrieves information from more and more external sources to increase its confidence level to determine whether the probability of a renovation will increase or decrease, wherein potential evidence is collected and where a timeline of unreported events with hard documentary evidence is provided.).
Rose discloses a system and method for extrapolating unknown/not yet verified information about a home by referring to reliable/verifiable sources of information. Despite this, Rose fails to explicitly disclose whether it is old and well-known in the art to train a machine learning algorithm to use other sources of information in order to extrapolate this information, such as, referring to other homes.
To be more specific, Rose fails to explicitly disclose:
training, by the one or more processors, a machine learning algorithm to determine a square footage of a property using the predictor variables of the first set of properties and the square footage of each of the first set of properties;
identifying, by one or more processors, a subject property different from the first set of properties
extracting, by the one or more processors, predictor variables of the subject property, wherein at least one of the predictor variables is for the feature which is extracted from the one or more aerial images of the subject property
applying, by the one or more processors, the predictor variables subject property to the trained machine learning algorithm to determine a square footage of the subject property (i.e. the unknown or inaccurate property parameter)
providing, by the one or more processors, the square footage of the subject property for display
However, Lookabough, which is also directed towards extrapolating unknown information about a home by referring to reliable sources of information, further teaches that other reliable well-known sources of information are other homes. Lookabough teaches that homes can have information that is common amongst themselves and, accordingly, it would have been obvious to compare one home with a similar (home) in order to extrapolate a particular piece of information for the home in question. Further still, Lookabough teaches that such processes can be performed by training a machine learning algorithm to learn what source of information can be referred to, as well as train the algorithm how to extrapolate the information.
(For support see: Page 16 Lines 22 – 32; Pages 17 – 18 Lines 23 – 1; Page 18 Lines 11 – 23; Page 19 Lines 11 – 17; Page 20 Lines 26 – 32; Page 21 Lines 26 – 37; Page 25 – 27 Lines 25 – 15; Page 30 Lines 3 – 24 wherein a plurality of properties within the vicinity of a subject property’s location are identified; Page 15 Lines 24 – 31; Pages 16 – 18 Lines 22 – 1; Page 18 Lines 11 – 23; Page 20 Lines 19 – 32; Page 21 Lines 26 – 37; Pages 26 – 27 Lines 25 – 15; Page 30 Lines 3 – 24; Page 36 – 37 Lines 34 – 8 wherein information of the plurality of properties is received)
More specifically, Lookabaugh teach that a plurality of parameters about the subject property can be estimated based on analyzing a plurality of homes within the vicinity of the subject property. Lookabaugh teaches that this can be performed if information about the subject property is missing and, in order to fill in the missing information, the system estimates the particular parameter, which can be, but is not limited to, year built, square footage, and build grade, wherein the estimated parameter is a weighted average of the other properties’ particular parameter that is being estimated. Lookabaugh further teaches that that determination can be performed by using k-nearest neighbor, closest properties by Euclidean distance, common geographic region such as a town or county, latitude/longitude, nearby properties, and number of desired other homes to compare the subject property with.
(For support see: Pages 17 – 18 Lines 7 – 23; Page 19 Lines 11 – 25; Page 30 Lines 3 – 24; Pages 36 – 37 Lines 34 – 8)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in the property analysis system and method of Rose with additional sources of information, such as, other homes that share certain characteristics of a home in question, as taught by Lookabaugh, since providing as much information as possible about a particular home of interest provides home buyers who may have a low attention span and low tolerance for latency, but are interested with completing a real estate transaction, with rapid feedback pertaining to potential real estate transactions that can be critical in ensuring that would-be customer remain engaged, increase user experience, estimate the fair value of a home, and facilitate a customer’s decision making process (Pages 10 – 11 Lines 25 – 29; Page 12 Lines 5 – 33, Pages 14 – 15 Lines 32 – 7; Pages 15 Lines 32 - 21).
In regards to claims 3, 10, 17, the combination of Rose and Lookabaugh discloses the computer-implemented method of claim 1 (the computer system of claim 8; the non-transitory computer-readable memory of claim 15), further comprising: applying, by the one or more processors, predictor variables of the subject property which are most likely to be predictive of year built to the trained machine learning algorithm to determine a year built of the subject property (Rose – ¶ 16, 215 wherein the system applies data from the aerial images to determine the street address of the property and to further determine the year it was built;
¶ 12, 13, 14, 15, 17, 18, 103, 104, 106, 121, 162, 180, 181, 182, 184, 215 wherein machine learning is trained to determine a property parameter, such as, but not limited to, the square footage of a property using data mining of external sources, such as, but not limited to, property records, listings, photographic images (e.g., satellite/aerial, street view, videos), characteristics/features/predictor variables of the home (determining that a garage has been converted to a “Granny Flat” by determining, i.e. predicting, that the home should have a garage and comparing this information to, for example, images to determine that the garage is no longer there, thereby increasing the livable square footage of the property), and etc. Rose further discloses that subsets of the feature values/predictor variables can be correlated with square footage by associating these two pieces of information with one another. The system also receives verified data and feeds back the verified data to improve its prediction over time).
In regards to claims 4, 11, 18, the combination of Rose and Lookabaugh discloses the computer-implemented method of claim 1 (the computer system of claim 8; the non-transitory computer-readable memory of claim 15), further comprising: applying, by the one or more processors, the predictor variables of the subject property which are most likely to be predictive of garage size to the trained machine learning algorithm to determine a garage size of the subject property (Rose – ¶ 12, 16, 75, 163 wherein aerial images are used by the machine learning algorithm to determine if there has been a garage conversion made to the property, a change in the square footage of the property (which is also based on data gathered from the aforementioned plurality of data sources), and the frequency in which a garage door is opening and closing. As a result, the system is able to determine if the garage no longer exists because it has been converted to the room, thereby increasing the square footage of the home while decreasing the square footage of the home. Additionally, the system is also able to determine if an addition was made to the home and can further utilize aerial images and other data to determine street and driveway parking changes, e.g., determine that a property had vehicle parking originally then later determining that there is driveway parking, the determination of an increase in the size of the property, and the identification of a garage and its use. The system is configured to identify physical changes to the property.).
In regards to claims 5, 12, 19, the combination of Rose and Lookabaugh discloses the computer-implemented method of claim 1 (the computer system of claim 8; the non-transitory computer-readable memory of claim 15), further comprising: gathering measurement data of an exterior of a structure of the subject property based upon the aerial images (Rose – ¶ 12, 75 wherein exterior images are used by the machine learning algorithm to determine if there has been a change in the square footage of the property (which is also based on data gathered from the aforementioned plurality of data sources)).
In regards to claims 6, 13, 20, the combination of Rose and Lookabaugh discloses the computer-implemented method of claim 1 (the computer system of claim 8; the non-transitory computer-readable memory of claim 15), further comprising:
receiving, at the one or more processors, home characteristic data for the subject property, wherein the features of the subject property include the home characteristic data
(Rose – ¶ 12, 13, 14, 103, 104, 106, 119, 121, 215 wherein machine learning is trained to determine a property characteristic/features, such as, but not limited to, square footage, year built, renovation information, and etc., of a property using data mining of external sources, such as, but not limited to, property records, listings, photographic images (e.g., satellite/aerial, street view, videos), and etc.).
Response to Arguments
Applicant's arguments filed 1/9/2026 have been fully considered but they are not persuasive.
Claim Objections
The objection to the claims has been withdrawn due to amendments.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The rejection has been updated to reflect newly presented amendments, namely, removing “Mathematical Concepts.” Accordingly, all arguments directed towards “Mathematical Concepts” are moot.
With that said, the Examiner asserts that the applicant’s application of Ex Parte Desjardens, hereinafter referred to as Desjardens, does not apply to the instant claimed invention. Unlike Desjardens, the claimed invention is not improving any technology, let alone machine learning techniques. The Examiner asserts that the use of machine learning has been recited at a high level of generality and applied to the abstract idea for the benefits that machine learning provides, i.e. faster, more efficient, and etc. The claimed invention is directed towards the collection and comparison of information and, based on a rule(s), identify options, in this case, having a human collect and look at a plurality of aerial images for a plurality of real estate properties, collect information that include an indication of the square footage of those plurality of properties, look/observe the images to extract some information about the properties, identify and collect aerial images of a property of interest, look/observe the image of the property of interest, compare the information between the plurality of properties and property of interest to determine, guess, estimate, or the like the square footage of the property of interest, creating a record/profile for the property, and displaying the results of the analysis along with its confidence level.
The Examiner asserts that Desjardens is directed towards improving machine learning technology, wherein the specification identified issues that arose in machine learning technology and provided an invention that resolves the identified issue in order to improve machine learning technology. However, neither the claimed invention or specification are concerned with improving machine learning technology, let alone identifying an issue that arose in the technology and providing a solution to resolve the issue in order to improve the technology. As stated in the rejection, the claimed invention is directed towards real estate evaluation and assessment by comparing a property of interest against other properties in order to evaluate how they compare with one another and assessing or determining a particular characteristic of the property of interest based on the comparison. As a non-limiting example, the claimed invention is evaluating how a profile for a 2000 colonial home compares with other homes and, based on this comparison, predict, estimate, guess, or the like missing information for the property of interest, e.g., although the property of interest does not list its square footage, based on how the property of interest compares with other homes it can be surmised that the property of interest has a square footage of 3000 sq. ft. The Examiner asserts that this is not an improvement to machine learning technology nor identifying and resolving an issue that arose in machine learning technology, but reciting machine learning technology at a high level of generality and applying it to the abstract idea for the benefits that the technology provides, as was discussed above.
Similarly, Example 39 does not apply because, again, the claimed invention is not directed towards identifying an issue that arose in machine learning technology, resolving the issue that arose in machine learning technology, improving machine learning technology, or deeply rooted in machine learning technology. Example 39 identified an issue that arose in digital imaging technology, specifically, digital facial images, and utilized machine learning technology to improve upon digital imaging technology. Unlike the claimed invention, Example 39 cannot be practically performed in the human mind and, again, Example 39 is directed towards improving technology while the claimed invention is not. Additionally, as has been stated in the rejection and in prior Office Actions, the Examiner has provided examples demonstrating that the claimed invention can, indeed, be practically performed by a human in their mind and/or through the aid of pen and paper, i.e. “Mental Processes”. Moreover, the claimed invention is also directed towards “Certain Methods of Organizing Human Activities” because it is directed towards fundamental economic practices and commercial interactions, in this case, real estate evaluation and assessment by collecting and comparing real estate property information and filling in missing information of a property of interest based on how it compares to other real estate properties.
With regards to “performing feature reduction to avoid overfitting,” the Examiner asserts that this has been recited at a high level of generality and applied to the high-level generic recitation of machine learning technology. The Examiner asserts that training a machine learning model on data that will provide useable results will result in performing feature reduction to avoid overfitting because it is not using data that will not help with the desired prediction and avoid overfitting because, again, it is not using data that will not help with the desired prediction. The Examiner asserts that the amendment, limitation, and claimed invention is an idea of a solution or outcome and fails to recite specific non-generic details that have not been recited at a high level of generality for how a solution to a problem is accomplished. The Examiner asserts that the claimed invention is result-oriented and simply reciting the type of data being used to train the machine learning model does not rise to the same level of demonstrating how the model is being trained so as to resolve an issue that arose in machine learning technology, but simply directed towards describing the environment of use for the machine learning technology and abstract idea. As stated above, machine learning has been recited at a high level of generality and applied to the abstract idea for the benefits that machine learning provides, i.e. faster, more efficient, and etc., and, therefore, Example 47, Claim 2, is a more appropriate comparison.
The claimed invention is not improving machine learning technology in order to provide better calculations, predictions, or the like, but improving upon the collection and comparison of information, as discussed above, by using generic machine learning because it is faster, more efficient, and etc. and describing what information the applicant believes, in their mind, is the better information that should be collected and compared in order to guess, estimate, predict, or the like a parameter that is being sought after based on how it compares against known information.
Rejection under 35 USC 102/103
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Pertinent Arguments
As stated above, the applicant’s arguments directed towards newly presented subject matter, i.e. “perform feature reduction to avoid overfitting”, is moot, but has been addressed in the rejection above.
With regards to the applicant’s remaining arguments, the Examiner asserts that they are similar, if not the same, as those that were previously presented in the Remarks received on 8/20/2025, and addressed in the Non-Final Office Action mailed on 9/23/2025. The Examiner asserts that the applicant does not provide any additional arguments nor address where the Examiner had erred in the response. Accordingly, with the exception of the newly presented subject matter, which has been addressed in the rejection above, the Examiner refers to and incorporates the response provided in the Non-Final Office Action mailed on 9/23/2025.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Hagey (4 Ways Realtors are using Artificial Intelligence in their Business); Metz (This AI tool writes real estate descriptions without ever stepping inside a home) – which disclose using machine learning to write real estate listings
Vignesh (The Perfect Fit for a DNN) – which discloses overfitting in machine learning
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 1/26/2026